Search Results for author:
Found 6 papers, 1 papers with code
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification
Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate, even more so if they deal with domain specific language.
GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation
Medical imaging is crucial for diagnosing, monitoring, and treating medical conditions.
Towards Foundation Models for Critical Care Time Series
This work aims to establish a foundation for training large-scale multi-variate time series models on critical care data and to provide a benchmark for machine learning models in transfer learning across hospitals to study and address distribution shift challenges.
Combining Data Generation and Active Learning for Low-Resource Question Answering
In this work, we propose a novel approach that combines data augmentation via question-answer generation with Active Learning to improve performance in low-resource settings, where the target domains are diverse in terms of difficulty and similarity to the source domain.
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning
State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small instance-dependent subgraphs.
History distribution matching method for predicting effectiveness of HIV combination therapies
This paper presents an approach that predicts the effectiveness of HIV combination therapies by simultaneously addressing several problems affecting the available HIV clinical data sets: the different treatment backgrounds of the samples, the uneven representation of the levels of therapy experience, the missing treatment history information, the uneven therapy representation and the unbalanced therapy outcome representation.
